---
title: "NvidiaChatGenerator"
id: nvidiachatgenerator
slug: "/nvidiachatgenerator"
description: "This Generator enables chat completion using Nvidia-hosted models."
---

# NvidiaChatGenerator

This Generator enables chat completion using Nvidia-hosted models.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)                                     |
| **Mandatory init variables**           | `api_key`: API key for the NVIDIA NIM. Can be set with `NVIDIA_API_KEY` env var.         |
| **Mandatory run variables**            | `messages`: A list of [ChatMessage](../../concepts/data-classes/chatmessage.mdx) objects                           |
| **Output variables**                   | `replies`: A list of [ChatMessage](../../concepts/data-classes/chatmessage.mdx) objects                            |
| **API reference**                      | [NVIDIA API](https://build.nvidia.com/models)                                            |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/nvidia |

</div>

## Overview

`NvidiaChatGenerator` enables chat completions using NVIDIA's generative models via the NVIDIA API. It is compatible with the [ChatMessage](../../concepts/data-classes/chatmessage.mdx) format for both input and output, ensuring seamless integration in chat-based pipelines.

You can use LLMs self-hosted with NVIDIA NIM or models hosted on the [NVIDIA API catalog](https://build.nvidia.com/explore/discover). The default model for this component is `meta/llama-3.1-8b-instruct`.

To use this integration, you must have a NVIDIA API key. You can provide it with the `NVIDIA_API_KEY` environment variable or by using a [Secret](../../concepts/secret-management.mdx).

### Tool Support

`NvidiaChatGenerator` supports function calling through the `tools` parameter, which accepts flexible tool configurations:

- **A list of Tool objects**: Pass individual tools as a list
- **A single Toolset**: Pass an entire Toolset directly
- **Mixed Tools and Toolsets**: Combine multiple Toolsets with standalone tools in a single list

This allows you to organize related tools into logical groups while also including standalone tools as needed.

```python
from haystack.tools import Tool, Toolset
from haystack_integrations.components.generators.nvidia import NvidiaChatGenerator

# Create individual tools
weather_tool = Tool(name="weather", description="Get weather info", ...)
news_tool = Tool(name="news", description="Get latest news", ...)

# Group related tools into a toolset
math_toolset = Toolset([add_tool, subtract_tool, multiply_tool])

# Pass mixed tools and toolsets to the generator
generator = NvidiaChatGenerator(
    tools=[math_toolset, weather_tool, news_tool]  # Mix of Toolset and Tool objects
)
```

For more details on working with tools, see the [Tool](../../tools/tool.mdx) and [Toolset](../../tools/toolset.mdx) documentation.

### Streaming

This generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) responses from the LLM. To enable streaming, pass a callable to the `streaming_callback` parameter during initialization.

## Usage

To start using `NvidiaChatGenerator`, first, install the `nvidia-haystack` package:

```shell
pip install nvidia-haystack
```

You can use the `NvidiaChatGenerator` with all the LLMs available in the [NVIDIA API catalog](https://docs.api.nvidia.com/nim/reference) or a model deployed with NVIDIA NIM. Follow the [NVIDIA NIM for LLMs Playbook](https://developer.nvidia.com/docs/nemo-microservices/inference/playbooks/nmi_playbook.html) to learn how to deploy your desired model on your infrastructure.

### On its own

 To use LLMs from the NVIDIA API catalog, you need to specify the correct `api_url` if needed (the default one is `https://integrate.api.nvidia.com/v1`), and your API key. You can get your API key directly from the [catalog website](https://build.nvidia.com/explore/discover).

```python
from haystack_integrations.components.generators.nvidia import NvidiaChatGenerator
from haystack.dataclasses import ChatMessage

generator = NvidiaChatGenerator(
    model="meta/llama-3.1-8b-instruct",  # or any supported NVIDIA model
    api_key=Secret.from_env_var("NVIDIA_API_KEY")
)

messages = [ChatMessage.from_user("What's Natural Language Processing? Be brief.")]
result = generator.run(messages)
print(result["replies"])
print(result["meta"])
```

With multimodal inputs:

```python
from haystack.dataclasses import ChatMessage, ImageContent
from haystack_integrations.components.generators.nvidia import NvidiaChatGenerator

llm = NvidiaChatGenerator(model="meta/llama-3.2-11b-vision-instruct")

image = ImageContent.from_file_path("apple.jpg")
user_message = ChatMessage.from_user(content_parts=[
	"What does the image show? Max 5 words.",
	image
	])

response = llm.run([user_message])["replies"][0].text
print(response)

# Red apple on straw.
```

### In a Pipeline

```python
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.nvidia import NvidiaChatGenerator
from haystack.utils import Secret

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component("llm", NvidiaChatGenerator(
    model="meta/llama-3.1-8b-instruct",
    api_key=Secret.from_env_var("NVIDIA_API_KEY")
))
pipe.connect("prompt_builder", "llm")

country = "Germany"
system_message = ChatMessage.from_system("You are an assistant giving out valuable information to language learners.")
messages = [system_message, ChatMessage.from_user("What's the official language of {{ country }}?")]

res = pipe.run(data={"prompt_builder": {"template_variables": {"country": country}, "template": messages}})
print(res)
```
